{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R8LfM1aIkXi8"
   },
   "source": [
    "# Explain Attacking BERT models using CAptum\n",
    "\n",
    "Captum is a PyTorch library to explain neural networks\n",
    "Here we show a minimal example using Captum to explain BERT models from TextAttack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ChpotvAVkXjO"
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QData/TextAttack/blob/master/docs/2notebook/Example_5_Explain_BERT.ipynb)\n",
    "\n",
    "[![View Source on GitHub](https://img.shields.io/badge/github-view%20source-black.svg)](https://github.com/QData/TextAttack/blob/master/docs/2notebook/Example_5_Explain_BERT.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "fhwb7F-_kXjT"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5CMINr0OkXjU",
    "outputId": "9885ca90-029a-482a-b4aa-f0e0b3013568"
   },
   "outputs": [],
   "source": [
    "from textattack.datasets import HuggingFaceDataset\n",
    "from textattack.models.wrappers import HuggingFaceModelWrapper\n",
    "from textattack.models.wrappers import ModelWrapper\n",
    "from transformers import AutoModelForSequenceClassification, AutoTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PgjHaPC3otbu",
    "outputId": "2508c192-d280-493d-d454-9396ab1f1762"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: captum in /p/qdata/jy2ma/miniconda3/envs/textattack-dev/lib/python3.8/site-packages (0.3.1)\n",
      "Requirement already satisfied: torch>=1.2 in /p/qdata/jy2ma/miniconda3/envs/textattack-dev/lib/python3.8/site-packages (from captum) (1.8.1)\n",
      "Requirement already satisfied: matplotlib in /p/qdata/jy2ma/miniconda3/envs/textattack-dev/lib/python3.8/site-packages (from captum) (3.4.1)\n",
      "Requirement already satisfied: numpy in /p/qdata/jy2ma/miniconda3/envs/textattack-dev/lib/python3.8/site-packages (from captum) (1.18.5)\n",
      "Requirement already satisfied: typing-extensions in /p/qdata/jy2ma/miniconda3/envs/textattack-dev/lib/python3.8/site-packages (from torch>=1.2->captum) (3.7.4.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /p/qdata/jy2ma/miniconda3/envs/textattack-dev/lib/python3.8/site-packages (from matplotlib->captum) (0.10.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /p/qdata/jy2ma/miniconda3/envs/textattack-dev/lib/python3.8/site-packages (from matplotlib->captum) (8.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /p/qdata/jy2ma/miniconda3/envs/textattack-dev/lib/python3.8/site-packages (from matplotlib->captum) (1.3.1)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /p/qdata/jy2ma/miniconda3/envs/textattack-dev/lib/python3.8/site-packages (from matplotlib->captum) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /p/qdata/jy2ma/miniconda3/envs/textattack-dev/lib/python3.8/site-packages (from matplotlib->captum) (2.8.1)\n",
      "Requirement already satisfied: six in /p/qdata/jy2ma/miniconda3/envs/textattack-dev/lib/python3.8/site-packages (from cycler>=0.10->matplotlib->captum) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "#Optional: Install dependency CAptum\n",
    "!pip install captum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "h5ejfwO3kXjX"
   },
   "outputs": [],
   "source": [
    "from captum.attr import IntegratedGradients, LayerConductance, LayerIntegratedGradients, LayerDeepLiftShap, InternalInfluence, LayerGradientXActivation\n",
    "from captum.attr import visualization as viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LfCIkViJkXjX",
    "outputId": "f61ea8fe-bd2b-4b89-df88-2d1a9dc52e49"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    "torch.cuda.set_device(device) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 555,
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      "3d2a7791c1b14a2b92602534936a07d0"
     ]
    },
    "id": "wq4Te1nmkXjY",
    "outputId": "29e86e23-cb40-48c5-a074-5fafbbdb1e33"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default\n",
      "Reusing dataset ag_news (/p/qdata/jy2ma/.cache/textattack/datasets/ag_news/default/0.0.0/0eeeaaa5fb6dffd81458e293dfea1adba2881ffcbdc3fb56baeb5a892566c29a)\n",
      "textattack: Loading \u001b[94mdatasets\u001b[0m dataset \u001b[94mag_news\u001b[0m, split \u001b[94mtrain\u001b[0m.\n"
     ]
    }
   ],
   "source": [
    "dataset = HuggingFaceDataset(\"ag_news\", None, \"train\")\n",
    "original_model = AutoModelForSequenceClassification.from_pretrained(\"textattack/bert-base-uncased-ag-news\")\n",
    "original_tokenizer = AutoTokenizer.from_pretrained(\"textattack/bert-base-uncased-ag-news\")\n",
    "model = HuggingFaceModelWrapper(original_model,original_tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "Ncxq0UCtkXjZ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'input_ids': tensor([[  101,  2813,  2358,  1012,  6468, 15020,  2067,  2046,  1996,  2304,\n",
      "          1006, 26665,  1007, 26665,  1011,  2460,  1011, 19041,  1010,  2813,\n",
      "          2395,  1005,  1055,  1040, 11101,  2989,  1032,  2316,  1997, 11087,\n",
      "          1011, 22330,  8713,  2015,  1010,  2024,  3773,  2665,  2153,  1012,\n",
      "           102,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "             0,     0,     0],\n",
      "        [  101, 18431,  2571,  3504,  2646,  3293, 13395,  1006, 26665,  1007,\n",
      "         26665,  1011,  2797,  5211,  3813, 18431,  2571,  2177,  1010,  1032,\n",
      "          2029,  2038,  1037,  5891,  2005,  2437,  2092,  1011, 22313,  1998,\n",
      "          5681,  1032,  6801,  3248,  1999,  1996,  3639,  3068,  1010,  2038,\n",
      "          5168,  2872,  1032,  2049, 29475,  2006,  2178,  2112,  1997,  1996,\n",
      "          3006,  1012,   102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "         0, 0, 0, 0, 0],\n",
      "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "         0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
      "         0, 0, 0, 0, 0],\n",
      "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "         1, 1, 1, 1, 1]])}\n"
     ]
    }
   ],
   "source": [
    "def get_text(tokenizer,input_ids,token_type_ids,attention_mask):\n",
    "    list_of_text = []\n",
    "    number = input_ids.size()[0]\n",
    "    for i in range(number):\n",
    "        ii = input_ids[i,].cpu().numpy()\n",
    "        tt = token_type_ids[i,]\n",
    "        am = attention_mask[i,]\n",
    "        txt = tokenizer.decode(ii, skip_special_tokens=True)\n",
    "        list_of_text.append(txt)\n",
    "    return list_of_text\n",
    "    \n",
    "sel =2\n",
    "batch_encoded = model.tokenizer([dataset[i][0]['text'] for i in range(sel)], padding=True, return_tensors=\"pt\")\n",
    "batch_encoded.to(device)\n",
    "labels = [dataset[i][1] for i in range(sel)]\n",
    "\n",
    "clone = deepcopy(model)\n",
    "clone.model.to(device)\n",
    "\n",
    "def calculate(input_ids,token_type_ids,attention_mask):\n",
    "    #convert back to list of text\n",
    "    return clone.model(input_ids,token_type_ids,attention_mask)[0]\n",
    "    \n",
    "# x = calculate(**batch_encoded)    \n",
    "\n",
    "lig = LayerIntegratedGradients(calculate, clone.model.bert.embeddings)\n",
    "# lig = InternalInfluence(calculate, clone.model.bert.embeddings)\n",
    "# lig = LayerGradientXActivation(calculate, clone.model.bert.embeddings)\n",
    "\n",
    "bsl = torch.zeros(batch_encoded['input_ids'].size()).type(torch.LongTensor).to(device)\n",
    "labels = torch.tensor(labels).to(device)\n",
    "\n",
    "attributions,delta = lig.attribute(inputs=batch_encoded['input_ids'],\n",
    "                              baselines=bsl,\n",
    "                              additional_forward_args=(batch_encoded['token_type_ids'], batch_encoded['attention_mask']),\n",
    "                              n_steps = 10,\n",
    "                              target = labels,\n",
    "                              return_convergence_delta=True\n",
    "                              )\n",
    "atts = attributions.sum(dim=-1).squeeze(0)\n",
    "atts = atts / torch.norm(atts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "ao-XxS1xkXja"
   },
   "outputs": [],
   "source": [
    "atts = attributions.sum(dim=-1).squeeze(0)\n",
    "atts = atts / torch.norm(atts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PebvHtSokXja",
    "outputId": "4dc376b6-cd03-4b13-d048-6267e772736a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "textattack: Unknown if model of class <class 'transformers.models.bert.modeling_bert.BertForSequenceClassification'> compatible with goal function <class 'textattack.goal_functions.classification.untargeted_classification.UntargetedClassification'>.\n"
     ]
    }
   ],
   "source": [
    "from textattack.attack_recipes import PWWSRen2019\n",
    "attack = PWWSRen2019.build(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NMhOiNr3k-R8",
    "outputId": "7ebd328c-fbcc-4bb3-f2c2-5b9326148681"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/10 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attack(\n",
      "  (search_method): GreedyWordSwapWIR(\n",
      "    (wir_method):  weighted-saliency\n",
      "  )\n",
      "  (goal_function):  UntargetedClassification\n",
      "  (transformation):  WordSwapWordNet\n",
      "  (constraints): \n",
      "    (0): RepeatModification\n",
      "    (1): StopwordModification\n",
      "  (is_black_box):  True\n",
      ") \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Succeeded / Failed / Skipped / Total] 1 / 0 / 0 / 1:  10%|█         | 1/10 [00:04<00:44,  4.91s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------- Result 1 ---------------------------------------------\n",
      "\u001b[94mBusiness (96%)\u001b[0m --> \u001b[35mSci/tech (68%)\u001b[0m\n",
      "\n",
      "Wall St. \u001b[94mBears\u001b[0m Claw Back Into the \u001b[94mBlack\u001b[0m (Reuters) Reuters - Short-sellers, Wall Street's \u001b[94mdwindling\u001b[0m\\\u001b[94mband\u001b[0m of ultra-cynics, are \u001b[94mseeing\u001b[0m \u001b[94mgreen\u001b[0m again.\n",
      "\n",
      "Wall St. \u001b[35msuffer\u001b[0m Claw Back Into the \u001b[35mlightlessness\u001b[0m (Reuters) Reuters - Short-sellers, Wall Street's \u001b[35mdwindle\u001b[0m\\\u001b[35misthmus\u001b[0m of ultra-cynics, are \u001b[35mexamine\u001b[0m \u001b[35mgreenish\u001b[0m again.\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Succeeded / Failed / Skipped / Total] 2 / 0 / 0 / 2:  20%|██        | 2/10 [00:10<00:40,  5.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------- Result 2 ---------------------------------------------\n",
      "\u001b[94mBusiness (100%)\u001b[0m --> \u001b[35mSci/tech (50%)\u001b[0m\n",
      "\n",
      "Carlyle Looks Toward Commercial Aerospace (Reuters) Reuters - Private \u001b[94minvestment\u001b[0m firm Carlyle Group,\\which has a reputation for \u001b[94mmaking\u001b[0m well-timed and occasionally\\controversial plays in the \u001b[94mdefense\u001b[0m industry, has quietly \u001b[94mplaced\u001b[0m\\its bets on another part of the market.\n",
      "\n",
      "Carlyle Looks Toward Commercial Aerospace (Reuters) Reuters - Private \u001b[35minvestiture\u001b[0m firm Carlyle Group,\\which has a reputation for \u001b[35mca-ca\u001b[0m well-timed and occasionally\\controversial plays in the \u001b[35mdenial\u001b[0m industry, has quietly \u001b[35msite\u001b[0m\\its bets on another part of the market.\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Succeeded / Failed / Skipped / Total] 3 / 0 / 0 / 3:  30%|███       | 3/10 [00:14<00:33,  4.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------- Result 3 ---------------------------------------------\n",
      "\u001b[94mBusiness (100%)\u001b[0m --> \u001b[35mSci/tech (100%)\u001b[0m\n",
      "\n",
      "Oil and Economy \u001b[94mCloud\u001b[0m Stocks' Outlook (Reuters) Reuters - Soaring crude prices plus worries\\about the economy and the outlook for \u001b[94mearnings\u001b[0m are expected to\\hang over the \u001b[94mstock\u001b[0m market next week during the depth of the\\summer doldrums.\n",
      "\n",
      "Oil and Economy \u001b[35mswarm\u001b[0m Stocks' Outlook (Reuters) Reuters - Soaring crude prices plus worries\\about the economy and the outlook for \u001b[35mlucre\u001b[0m are expected to\\hang over the \u001b[35mgillyflower\u001b[0m market next week during the depth of the\\summer doldrums.\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Succeeded / Failed / Skipped / Total] 4 / 0 / 0 / 4:  40%|████      | 4/10 [00:18<00:27,  4.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------- Result 4 ---------------------------------------------\n",
      "\u001b[94mBusiness (78%)\u001b[0m --> \u001b[91mWorld (58%)\u001b[0m\n",
      "\n",
      "Iraq \u001b[94mHalts\u001b[0m Oil Exports from Main Southern Pipeline (Reuters) Reuters - Authorities have halted oil export\\\u001b[94mflows\u001b[0m from the main pipeline in southern \u001b[94mIraq\u001b[0m after\\intelligence showed a rebel militia could \u001b[94mstrike\u001b[0m\\infrastructure, an oil official said on Saturday.\n",
      "\n",
      "Iraq \u001b[91mkibosh\u001b[0m Oil Exports from Main Southern Pipeline (Reuters) Reuters - Authorities have halted oil export\\\u001b[91mhang\u001b[0m from the main pipeline in southern \u001b[91mIrak\u001b[0m after\\intelligence showed a rebel militia could \u001b[91mfall\u001b[0m\\infrastructure, an oil official said on Saturday.\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Succeeded / Failed / Skipped / Total] 5 / 0 / 0 / 5:  50%|█████     | 5/10 [00:23<00:23,  4.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------- Result 5 ---------------------------------------------\n",
      "\u001b[94mBusiness (99%)\u001b[0m --> \u001b[91mWorld (82%)\u001b[0m\n",
      "\n",
      "Oil prices soar to all-time record, posing new menace to \u001b[94mUS\u001b[0m \u001b[94meconomy\u001b[0m (AFP) AFP - Tearaway world oil prices, toppling records and straining wallets, present a new economic menace barely three months before the US presidential elections.\n",
      "\n",
      "Oil prices soar to all-time record, posing new menace to \u001b[91muranium\u001b[0m \u001b[91mthriftiness\u001b[0m (AFP) AFP - Tearaway world oil prices, toppling records and straining wallets, present a new economic menace barely three months before the US presidential elections.\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Succeeded / Failed / Skipped / Total] 6 / 0 / 0 / 6:  60%|██████    | 6/10 [00:30<00:20,  5.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------- Result 6 ---------------------------------------------\n",
      "\u001b[94mBusiness (100%)\u001b[0m --> \u001b[35mSci/tech (55%)\u001b[0m\n",
      "\n",
      "\u001b[94mStocks\u001b[0m \u001b[94mEnd\u001b[0m Up, But Near Year Lows (Reuters) Reuters - \u001b[94mStocks\u001b[0m \u001b[94mended\u001b[0m slightly higher on Friday\\but \u001b[94mstayed\u001b[0m \u001b[94mnear\u001b[0m \u001b[94mlows\u001b[0m for the year as \u001b[94moil\u001b[0m prices surged past  #36;\u001b[94m46\u001b[0m\\a \u001b[94mbarrel\u001b[0m, \u001b[94moffsetting\u001b[0m a \u001b[94mpositive\u001b[0m \u001b[94moutlook\u001b[0m from computer \u001b[94mmaker\u001b[0m\\Dell Inc. (DELL.O)\n",
      "\n",
      "\u001b[35mstock\u001b[0m \u001b[35mterminate\u001b[0m Up, But Near Year Lows (Reuters) Reuters - \u001b[35minventory\u001b[0m \u001b[35mterminate\u001b[0m slightly higher on Friday\\but \u001b[35mcontinue\u001b[0m \u001b[35mvirtually\u001b[0m \u001b[35mmoo\u001b[0m for the year as \u001b[35membrocate\u001b[0m prices surged past  #36;\u001b[35mxlvi\u001b[0m\\a \u001b[35mdrum\u001b[0m, \u001b[35mcancel\u001b[0m a \u001b[35mincontrovertible\u001b[0m \u001b[35mmindset\u001b[0m from computer \u001b[35mCreator\u001b[0m\\Dell Inc. (DELL.O)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Succeeded / Failed / Skipped / Total] 7 / 0 / 0 / 7:  70%|███████   | 7/10 [00:36<00:15,  5.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------- Result 7 ---------------------------------------------\n",
      "\u001b[94mBusiness (100%)\u001b[0m --> \u001b[91mWorld (76%)\u001b[0m\n",
      "\n",
      "Money Funds \u001b[94mFell\u001b[0m in \u001b[94mLatest\u001b[0m \u001b[94mWeek\u001b[0m (AP) AP - \u001b[94mAssets\u001b[0m of the nation's retail money \u001b[94mmarket\u001b[0m \u001b[94mmutual\u001b[0m funds \u001b[94mfell\u001b[0m by  #36;1.17 \u001b[94mbillion\u001b[0m in the latest week to  #36;849.98 \u001b[94mtrillion\u001b[0m, the Investment Company \u001b[94mInstitute\u001b[0m \u001b[94msaid\u001b[0m Thursday.\n",
      "\n",
      "Money Funds \u001b[91mhide\u001b[0m in \u001b[91mup-to-the-minute\u001b[0m \u001b[91mhebdomad\u001b[0m (AP) AP - \u001b[91masset\u001b[0m of the nation's retail money \u001b[91mcommercialise\u001b[0m \u001b[91mcommon\u001b[0m funds \u001b[91mcruel\u001b[0m by  #36;1.17 \u001b[91mgazillion\u001b[0m in the latest week to  #36;849.98 \u001b[91m1000000000000\u001b[0m, the Investment Company \u001b[91mconstitute\u001b[0m \u001b[91mstate\u001b[0m Thursday.\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Succeeded / Failed / Skipped / Total] 8 / 0 / 0 / 8:  80%|████████  | 8/10 [00:43<00:10,  5.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------- Result 8 ---------------------------------------------\n",
      "\u001b[94mBusiness (100%)\u001b[0m --> \u001b[91mWorld (76%)\u001b[0m\n",
      "\n",
      "\u001b[94mFed\u001b[0m \u001b[94mminutes\u001b[0m \u001b[94mshow\u001b[0m \u001b[94mdissent\u001b[0m over inflation (USATODAY.com) USATODAY.com - Retail sales \u001b[94mbounced\u001b[0m \u001b[94mback\u001b[0m a bit in July, and \u001b[94mnew\u001b[0m claims for jobless \u001b[94mbenefits\u001b[0m fell \u001b[94mlast\u001b[0m \u001b[94mweek\u001b[0m, the government said Thursday, indicating the economy is \u001b[94mimproving\u001b[0m from a midsummer slump.\n",
      "\n",
      "\u001b[91meat\u001b[0m \u001b[91mhour\u001b[0m \u001b[91mtestify\u001b[0m \u001b[91mprotest\u001b[0m over inflation (USATODAY.com) USATODAY.com - Retail sales \u001b[91mbound\u001b[0m \u001b[91mhind\u001b[0m a bit in July, and \u001b[91myoung\u001b[0m claims for jobless \u001b[91mwelfare\u001b[0m fell \u001b[91mdeath\u001b[0m \u001b[91mworkweek\u001b[0m, the government said Thursday, indicating the economy is \u001b[91mamend\u001b[0m from a midsummer slump.\n",
      "\n",
      "\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[Succeeded / Failed / Skipped / Total] 9 / 0 / 0 / 9:  90%|█████████ | 9/10 [00:49<00:05,  5.51s/it]"
     ]
    },
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     "output_type": "stream",
     "text": [
      "--------------------------------------------- Result 9 ---------------------------------------------\n",
      "\u001b[94mBusiness (100%)\u001b[0m --> \u001b[35mSci/tech (100%)\u001b[0m\n",
      "\n",
      "Safety \u001b[94mNet\u001b[0m (Forbes.com) Forbes.com - After earning a PH.D. in Sociology, Danny Bazil Riley started to work as the general manager at a commercial real estate firm at an annual base salary of  #36;70,000. Soon after, a financial planner stopped by his desk to drop off brochures about insurance benefits available through his employer. But, at 32, \"buying insurance was the furthest thing from my mind,\" says Riley.\n",
      "\n",
      "Safety \u001b[35mcyberspace\u001b[0m (Forbes.com) Forbes.com - After earning a PH.D. in Sociology, Danny Bazil Riley started to work as the general manager at a commercial real estate firm at an annual base salary of  #36;70,000. Soon after, a financial planner stopped by his desk to drop off brochures about insurance benefits available through his employer. But, at 32, \"buying insurance was the furthest thing from my mind,\" says Riley.\n",
      "\n",
      "\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[Succeeded / Failed / Skipped / Total] 10 / 0 / 0 / 10: 100%|██████████| 10/10 [00:53<00:00,  5.32s/it]"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "--------------------------------------------- Result 10 ---------------------------------------------\n",
      "\u001b[94mBusiness (100%)\u001b[0m --> \u001b[91mWorld (71%)\u001b[0m\n",
      "\n",
      "Wall St. Bears \u001b[94mClaw\u001b[0m Back Into the Black  \u001b[94mNEW\u001b[0m YORK (Reuters) - Short-sellers, Wall Street's dwindling  band of ultra-cynics, are seeing green again.\n",
      "\n",
      "Wall St. Bears \u001b[91mchela\u001b[0m Back Into the Black  \u001b[91mnovel\u001b[0m YORK (Reuters) - Short-sellers, Wall Street's dwindling  band of ultra-cynics, are seeing green again.\n",
      "\n",
      "\n",
      "\n",
      "+-------------------------------+--------+\n",
      "| Attack Results                |        |\n",
      "+-------------------------------+--------+\n",
      "| Number of successful attacks: | 10     |\n",
      "| Number of failed attacks:     | 0      |\n",
      "| Number of skipped attacks:    | 0      |\n",
      "| Original accuracy:            | 100.0% |\n",
      "| Accuracy under attack:        | 0.0%   |\n",
      "| Attack success rate:          | 100.0% |\n",
      "| Average perturbed word %:     | 16.33% |\n",
      "| Average num. words per input: | 38.5   |\n",
      "| Avg num queries:              | 331.8  |\n",
      "+-------------------------------+--------+\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fe2795db1c0>,\n",
       " <textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fe278e48ee0>,\n",
       " <textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fe276e05f40>,\n",
       " <textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fe25eb99f70>,\n",
       " <textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fe26ac07eb0>,\n",
       " <textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fe25f083d60>,\n",
       " <textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fe269c1c040>,\n",
       " <textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fe2780587f0>,\n",
       " <textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fe2673beee0>,\n",
       " <textattack.attack_results.successful_attack_result.SuccessfulAttackResult at 0x7fe26b26a940>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from textattack import Attacker\n",
    "\n",
    "attacker = Attacker(attack, dataset)\n",
    "attacker.attack_dataset()"
   ]
  }
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